1 # coding: utf-8 2 3 # In[3]: 4 5 6 import numpy as np 7 import matplotlib.pyplot as plt 8 9 def estimate_coefficients(x, y): 10 # size of the dataset OR number of observations/points 11 n = np.size(x) 12 13 # mean of x and y 14 # Since we are using numpy just calling mean on numpy is sufficient 15 mean_x, mean_y = np.mean(x), np.mean(y) 16 17 # calculating cross-deviation and deviation about x 18 SS_xy = np.sum(y*x - n*mean_y*mean_x) 19 SS_xx = np.sum(x*x - n*mean_x*mean_x) 20 21 # calculating regression coefficients 22 b_1 = SS_xy / SS_xx 23 b_0 = mean_y - b_1*mean_x 24 25 return(b_0, b_1) 26 27 # x,y are the location of points on graph 28 # color of the points change it to red blue orange play around 29 30 31 32 def plot_regression_line(x, y, b): 33 # plotting the points as per dataset on a graph 34 plt.scatter(x, y, color = "m",marker = "o", s = 30) 35 36 # predicted response vector 37 y_pred = b[0] + b[1]*x 38 39 # plotting the regression line 40 plt.plot(x, y_pred, color = "g") 41 42 # putting labels for x and y axis 43 plt.xlabel('Size') 44 plt.ylabel('Cost') 45 46 # function to show plotted graph 47 plt.show() 48 49 50 51 52 53 def main(): 54 # Datasets which we create 55 x = np.array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) 56 y = np.array([300, 350, 500, 700, 800, 850, 900, 900, 1000, 1200]) 57 58 # estimating coefficients 59 b = estimate_coefficients(x, y) 60 print("Estimated coefficients: b_0 = {} b_1 = {}".format(b[0], b[1])) 61 62 # plotting regression line 63 plot_regression_line(x, y, b) 64 65 66 if __name__ == "__main__": 67 main()